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The BRIDGE HadCM3 family of climate models: HadCM3@Bristol v1.0

Valdes, Paul J., Armstrong, Edward, Badger, Marcus P. S., Bradshaw, Catherine D., Bragg, Fran, Crucifix, Michel, Davies-Barnard, Taraka, Day, Jonathan J., Farnsworth, Alex, Gordon, Chris, Hopcroft, Peter O., Kennedy, Alan T., Lord, Natalie S., Lunt, Dan J., Marzocchi, Alice, Parry, Louise M., Pope, Vicky, Roberts, William H. G., Stone, Emma J., Tourte, Gregory J. L., Williams, Jonny H. T.·2017·Geoscientific Model Development
DOI 10.5194/gmd-10-3715-2017

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Abstract

Abstract. Understanding natural and anthropogenic climate change processes involves using computational models that represent the main components of the Earth system: the atmosphere, ocean, sea ice, and land surface. These models have become increasingly computationally expensive as resolution is increased and more complex process representations are included. However, to gain robust insight into how climate may respond to a given forcing, and to meaningfully quantify the associated uncertainty, it is often required to use either or both ensemble approaches and very long integrations. For this reason, more computationally efficient models can be very valuable tools. Here we provide a comprehensive overview of the suite of climate models based around the HadCM3 coupled general circulation model. This model was developed at the UK Met Office and has been heavily used during the last 15 years for a range of future (and past) climate change studies, but has now been largely superseded for many scientific studies by more recently developed models. However, it continues to be extensively used by various institutions, including the BRIDGE (Bristol Research Initiative for the Dynamic Global Environment) research group at the University of Bristol, who have made modest adaptations to the base HadCM3 model over time. These adaptations mean that the original documentation is not entirely representative, and several other relatively undocumented configurations are in use. We therefore describe the key features of a number of configurations of the HadCM3 climate model family, which together make up HadCM3@Bristol version 1.0. In order to differentiate variants that have undergone development at BRIDGE, we have introduced the letter B into the model nomenclature. We include descriptions of the atmosphere-only model (HadAM3B), the coupled model with a low-resolution ocean (HadCM3BL), the high-resolution atmosphere-only model (HadAM3BH), and the regional model (HadRM3B). These also include three versions of the land surface scheme. By comparing with observational datasets, we show that these models produce a good representation of many aspects of the climate system, including the land and sea surface temperatures, precipitation, ocean circulation, and vegetation. This evaluation, combined with the relatively fast computational speed (up to 1000 times faster than some CMIP6 models), motivates continued development and scientific use of the HadCM3B family of coupled climate models, predominantly for quantifying uncertainty and for long multi-millennial-scale simulations.

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Figures

Figure 1 Land-sea mask and orography (sea coloured grey, land height in metres) for four configurations of HadRM3B. (a) shows the standard European domain at 0.44 resolution, (b) shows the equivalent domain for East Asia, (c) shows a configuration for the Arctic and Svalbard at 0.22 (as used in Day et al., 2012), and (d) a North America/European configuration for the early Cretaceous at 0.44 resolution (as used in Haywood et al., 2004).
Figure 1 Land-sea mask and orography (sea coloured grey, land height in metres) for four configurations of HadRM3B. (a) shows the standard European domain at 0.44 resolution, (b) shows the equivalent domain for East Asia, (c) shows a configuration for the Arctic and Svalbard at 0.22 (as used in Day et al., 2012), and (d) a North America/European configuration for the early Cretaceous at 0.44 resolution (as used in Haywood et al., 2004).CC-BY · From the PDF
Figure 2 Relative error measure of the CMIP5 models (21 in total; in black) and the BRIDGE models (4 in total; in red) performance. Error measure is calculated from a time-space root-mean square error (RMSE) of contemporary and predecessor CMIP5 model historical climatological (1980-2005) seasonal cycle simulations and BRIDGE pre-industrial seasonal cycle climatologies against observations (1980-2005) for a set of nine different atmospheric variables. Error for each individual variable is characterised as a relative error by normalising the result of the median error of all model results (Gleckler et al., 2008); the BRIDGE models are not included in the mean/median error. For instance, a value of 0.20 indicates that a model's RMSE is 20 % larger than the median CMIP5 error for that variable, whereas a value of 0.20 means the error is 20 % smaller than the median error. The diagonal split grid square shows the relative error for the reference observed/reanalysis dataset (lower right triangle) and the alternative dataset (top left triangle). White triangles/boxes indicate where no data were available. Evaluated global atmospheric variables are TOA outgoing all-sky short-wave radiation (rsut_Glob), TOA outgoing all-sky outgoing long-wave radiation (rlut_Glob), precipitation (pr_Glob), near-surface temperature (tas_Glob), specific humidity at 400 hPa (hus_Glob-400), geopotential at 500 hPa (zg_Glob-500), V-wind at 200 hPa (va_Glob-200), V-wind at 850 hPa (va_Glob-850), U-wind at 200 hPa (ua_Glob-200), U-wind at 850 hPa (ua_Glob-850), temperature at 200 hPa (ta_Glob-200), and temperature at 850 hPa (ta_Glob-850).
Figure 2 Relative error measure of the CMIP5 models (21 in total; in black) and the BRIDGE models (4 in total; in red) performance. Error measure is calculated from a time-space root-mean square error (RMSE) of contemporary and predecessor CMIP5 model historical climatological (1980-2005) seasonal cycle simulations and BRIDGE pre-industrial seasonal cycle climatologies against observations (1980-2005) for a set of nine different atmospheric variables. Error for each individual variable is characterised as a relative error by normalising the result of the median error of all model results (Gleckler et al., 2008); the BRIDGE models are not included in the mean/median error. For instance, a value of 0.20 indicates that a model's RMSE is 20 % larger than the median CMIP5 error for that variable, whereas a value of 0.20 means the error is 20 % smaller than the median error. The diagonal split grid square shows the relative error for the reference observed/reanalysis dataset (lower right triangle) and the alternative dataset (top left triangle). White triangles/boxes indicate where no data were available. Evaluated global atmospheric variables are TOA outgoing all-sky short-wave radiation (rsut_Glob), TOA outgoing all-sky outgoing long-wave radiation (rlut_Glob), precipitation (pr_Glob), near-surface temperature (tas_Glob), specific humidity at 400 hPa (hus_Glob-400), geopotential at 500 hPa (zg_Glob-500), V-wind at 200 hPa (va_Glob-200), V-wind at 850 hPa (va_Glob-850), U-wind at 200 hPa (ua_Glob-200), U-wind at 850 hPa (ua_Glob-850), temperature at 200 hPa (ta_Glob-200), and temperature at 850 hPa (ta_Glob-850).CC-BY · From the PDF
Figure 3 (a) The difference between the annual mean surface air temperature (in C) of HadCM3B-M1 and the CRU CL v2.0 for the period 1960-1990 regridded onto the HadCM3B-M1 grid, (b) As (a) for the HadCM3B-M2.1a version, (c) As (a) but for HadCM3B-M2.2, (d) As (a) but for the HadCM3BL-M2.1aN version, (e) As (a) but for HadAM3B-M2.1a, (f) As (a) but for HadAM3BH-M2.1a, (g) As (a) but for FAMOUS-M1, and (h) As (a) but for FAMOUS-M2.2. Panels (i, j, k, l) show comparable results for four CMIP5 models, ACCESS1-0, CCSM4, GISS-E2-H and IPSL-CM5A-LR respectively. These were chosen to represent two models which were above the CMIP5 average in terms of their RMSE with respect to surface air temperature, and two models which were below average. All differences are calculated by regridding the CRU data onto the corresponding model grid, using simple bi-linear interpolation.
Figure 3 (a) The difference between the annual mean surface air temperature (in C) of HadCM3B-M1 and the CRU CL v2.0 for the period 1960-1990 regridded onto the HadCM3B-M1 grid, (b) As (a) for the HadCM3B-M2.1a version, (c) As (a) but for HadCM3B-M2.2, (d) As (a) but for the HadCM3BL-M2.1aN version, (e) As (a) but for HadAM3B-M2.1a, (f) As (a) but for HadAM3BH-M2.1a, (g) As (a) but for FAMOUS-M1, and (h) As (a) but for FAMOUS-M2.2. Panels (i, j, k, l) show comparable results for four CMIP5 models, ACCESS1-0, CCSM4, GISS-E2-H and IPSL-CM5A-LR respectively. These were chosen to represent two models which were above the CMIP5 average in terms of their RMSE with respect to surface air temperature, and two models which were below average. All differences are calculated by regridding the CRU data onto the corresponding model grid, using simple bi-linear interpolation.CC-BY · From the PDF
Figure 4 As for Fig. 3 but showing the difference in mean annual precipitation, expressed as a % difference to the CRU CL v2.0 observations.
Figure 4 As for Fig. 3 but showing the difference in mean annual precipitation, expressed as a % difference to the CRU CL v2.0 observations.CC-BY · From the PDF
Figure 5 The annual mean northward heat transport in total, the atmosphere, and the ocean. The black line shows the observational estimate, blue HadCM3B-M2.1a, green HadCM3BL-M2.1a, and red FAMOUS-M2.2; grey lines show transports for a selection of CMIP5 models. These transports are calculated as the implied heat transports from the TOA and surface energy fluxes (see Trenberth and Fasullo, 2013, for details). Observational estimates for the total transport are derived from the CERES data.
Figure 5 The annual mean northward heat transport in total, the atmosphere, and the ocean. The black line shows the observational estimate, blue HadCM3B-M2.1a, green HadCM3BL-M2.1a, and red FAMOUS-M2.2; grey lines show transports for a selection of CMIP5 models. These transports are calculated as the implied heat transports from the TOA and surface energy fluxes (see Trenberth and Fasullo, 2013, for details). Observational estimates for the total transport are derived from the CERES data.CC-BY · From the PDF
Figure 6 Annual mean sea surface temperature differences (in C) for a range of coupled model simulations, and also for the same four CMIP5 models used in Figs. 3 and 4. The observational dataset is the Levitus World Ocean Atlas (2009) (Locarnini et al., 2010). The figure shows the difference in SST between model and observations for (a) HadCM3B-M1, (b) HadCM3B-M2.1a, (c) HadCM3B-M2.2, (d) HadCM3BL-M2.1a, (e) FAMOUS-M1, (f) FAMOUS-M2.2, (g) ACCESS1.0, (h) CCSM4, (i) GISS-E2-H, and (j) IPSL-CM5A-LR. Model output is regridded to the same resolution of the observations.
Figure 6 Annual mean sea surface temperature differences (in C) for a range of coupled model simulations, and also for the same four CMIP5 models used in Figs. 3 and 4. The observational dataset is the Levitus World Ocean Atlas (2009) (Locarnini et al., 2010). The figure shows the difference in SST between model and observations for (a) HadCM3B-M1, (b) HadCM3B-M2.1a, (c) HadCM3B-M2.2, (d) HadCM3BL-M2.1a, (e) FAMOUS-M1, (f) FAMOUS-M2.2, (g) ACCESS1.0, (h) CCSM4, (i) GISS-E2-H, and (j) IPSL-CM5A-LR. Model output is regridded to the same resolution of the observations.CC-BY · From the PDF
Figure 7 As Fig. 6 but showing the differences in sea surface salinity (in gkg 1 ) between models and observations. (a) HadCM3B-M1, (b) HadCM3B-M2.1a, (c) HadCM3B-M2.2, (d) HadCM3BL-M2.1a, (e) FAMOUS-M1, (f) FAMOUS-M2.2, (g) GISS-E2-H, and (h) IPSLCM5A-LR. The observational dataset is the Levitus World Ocean Atlas (2009) (Antonov et al., 2010). Model output is regridded to the same resolution of the observations.
Figure 7 As Fig. 6 but showing the differences in sea surface salinity (in gkg 1 ) between models and observations. (a) HadCM3B-M1, (b) HadCM3B-M2.1a, (c) HadCM3B-M2.2, (d) HadCM3BL-M2.1a, (e) FAMOUS-M1, (f) FAMOUS-M2.2, (g) GISS-E2-H, and (h) IPSLCM5A-LR. The observational dataset is the Levitus World Ocean Atlas (2009) (Antonov et al., 2010). Model output is regridded to the same resolution of the observations.CC-BY · From the PDF
Figure 8 Annual depth profiles of the Atlantic Meridional Overturning Circulation (AMOC) at 26.25 N showing the range of values for variants of HadCM3B, HadCM3BL, and FAMOUS. Annual data from the RAPID array at 26.5 N are highlighted in grey. The depth at which the AMOC reaches its maximum is indicated with a point.
Figure 8 Annual depth profiles of the Atlantic Meridional Overturning Circulation (AMOC) at 26.25 N showing the range of values for variants of HadCM3B, HadCM3BL, and FAMOUS. Annual data from the RAPID array at 26.5 N are highlighted in grey. The depth at which the AMOC reaches its maximum is indicated with a point.CC-BY · From the PDF
Figure 9 Maps of the dominant plant functional type for observations (a) and model simulations of the pre-industrial. The models shown are (b) HadCM3B-M2.1aD (c) HadCM3BL-M2.1aD and (d) HadAM3B-M2.1aD. The observed dataset for comparison is Ramankutty and Foley (1999).
Figure 9 Maps of the dominant plant functional type for observations (a) and model simulations of the pre-industrial. The models shown are (b) HadCM3B-M2.1aD (c) HadCM3BL-M2.1aD and (d) HadAM3B-M2.1aD. The observed dataset for comparison is Ramankutty and Foley (1999).CC-BY · From the PDF
Figure 10 The latitudinal average NPP in gCm 2 yr 1 . CMIP5 models without dynamic vegetation plotted here are CCSM4 and IPSL-CM5A-LR. CMIP5 models with dynamic vegetation plotted here are MIROC-ESM and MPI-ESM.
Figure 10 The latitudinal average NPP in gCm 2 yr 1 . CMIP5 models without dynamic vegetation plotted here are CCSM4 and IPSL-CM5A-LR. CMIP5 models with dynamic vegetation plotted here are MIROC-ESM and MPI-ESM.CC-BY · From the PDF

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Metadata

Authors
Valdes, P. J., Armstrong, E., Badger, M. P. S., Bradshaw, C. D., Bragg, F., Crucifix, M., Davies-Barnard, T., Day, J. J., Farnsworth, A., Gordon, C., Hopcroft, P. O., Kennedy, A. T., Lord, N. S., Lunt, D. J., Marzocchi, A., Parry, L. M., Pope, V., Roberts, W. H. G., Stone, E. J., Tourte, G. J. L., & Williams, J. H. T.
ORCID
Venue
Geoscientific Model Development
Publisher
Copernicus GmbH
Published
2017-10-12
Volume
10
Issue
10
Pages
3715-3743
ISSN
1991-9603
DOI
10.5194/gmd-10-3715-2017
Cited by
286
References
3
Language
en
Licence
creativecommons.org/licenses/by/3.0/ (vor)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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